This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...

Image Denoising with Autoencoders in R (University Project) Built a convolutional autoencoder in R using Keras/TensorFlow to perform image denoising on MNIST and CIFAR-10 datasets with varying levels ...

Abstract: We investigate the impact of noise on time-reversal imaging and propose an approach that significantly enhances the detection of objects in noisy environments. Our method involves the ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.5c00825. Data acquisition process and generation of a ...

A group of scientists led by researchers from the University of New South Wales (UNSW) in Australia has developed a novel deep-learning method for denoising outdoor electroluminescence (EL) images of ...

X-ray computed tomography (CT) is widely used in clinical practice for screening and diagnosing patients, as it enables the acquisition of high-resolution images of internal tissues and organs in a ...

Abstract: Industrial process data are usually affected by random and gross errors leading to deviation from the true value and violation of process constraints. Traditional data reconciliation methods ...

The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised ...

Anomaly detection is a typical binary classification problem under the condition of unbalanced samples, which has been widely used in various fields of data mining. For example, it can help detect ...